Rebecca F. Bruce

Learning Subjective Language

Subjectivity in natural language refers to aspects of language used to express opinions, evaluations, and speculations. There are numerous natural language processing applications for which subjectivity analysis is relevant, including information extraction and text categorization. The goal of this work is learning subjective language from corpora. Clues of subjectivity are generated and tested, including low-frequency words, collocations, and adjectives and verbs identified using distributional similarity. The features are also examined working together in concert. The features, generated from different data sets using different procedures, exhibit consistency in performance in that they all do better and worse on the same data sets. In addition, this article shows that the density of subjectivity clues in the surrounding context strongly affects how likely it is that a word is subjective, and it provides the results of an annotation study assessing the subjectivity of sentences with high-density features. Finally, the clues are used to perform opinion piece recognition (a type of text categorization and genre detection) to demonstrate the utility of the knowledge acquired in this article.

Development and Use of a Gold-Standard Data Set for Subjectivity Classifications

This paper presents a case study of analyzing and improving intercoder reliability in discourse tagging using statistical techniques. Bias-corrected tags are formulated and successfully used to guide a revision of the coding manual and develop an automatic classifier.

Word-Sense Disambiguation Using Decomposable Models

Most probabilistic classifiers used for word-sense disambiguation have either been based on only one contextual feature or have used a model that is simply assumed to characterize the interdependencies among multiple contextual features. In this paper, a different approach to formulating a probabilistic model is presented along with a case study of the performance of models produced in this manner for the disambiguation of the noun interest. We describe a method for formulating probabilistic models that use multiple contextual features for word-sense disambiguation, without requiring untested assumptions regarding the form of the model. Using this approach, the joint distribution of all variables is described by only the most systematic variable interactions, thereby limiting the number of parameters to be estimated, supporting computational efficiency, and providing an understanding of the data.

Recognizing subjectivity: a case study in manual tagging

In this paper, we describe a case study of a sentence-level categorization in which tagging instructions are developed and used by four judges to classify clauses from the Wall Street Journal as either subjective or objective. Agreement among the four judges is analyzed, and based on that analysis, each clause is given a final classification. To provide empirical support for the classifications, correlations are assessed in the data between the subjective category and a basic semantic class posited by Quirk, Greenbaum, Leech and Svartvik (1985).

A corpus study of evaluative and speculative language

This paper presents a corpus study of evaluative and speculative language. Knowledge of such language would be useful in many applications, such as text categorization and summarization. Analyses of annotator agreement and of characteristics of subjective language are performed. This study yields knowledge needed to design effective machine learning systems for identifying subjective language.

Is there content in empty heads

We describe a technique for automatically constructing a taxonomy of word senses from a machine readable dictionary. Previous taxonomies developed from dictionaries have two properties in common. First, they are based on a somewhat loosely defined notion of the IS-A relation. Second, they require human intervention to identify the sense of the genus term being used. We believe that for taxonomies of this type to serve a useful role in subsequent natural language processing tasks, the taxonomy must be based on a consistent use of the IS-A relation which allows inheritance and transitivity. We show that hierarchies of this type can be automatically constructed, by using the semantic category codes and the subject codes of the Longman Dictionary of Contemporary English (LDOCE) to disambiguate the genus terms in noun definitions. In addition, we discuss how certain genus terms give rise to other semantic relations between definitions.

Genus disambiguation: a study in weighted preference

The automatic construction of an IS_A taxonomy of noun senses from a machine readable dictionary (MRD) has long been sought, but achieved with only limited success. The task requires the solution to two problems: 1) To define an algorithm to automatically identify the genus or hypernym of a noun definition, and 2) to define an algorithm for lexical disambiguation of the genus term. In the last few years, effective methods for solving the first problem have been developed, but the problem of creating an algorithm for lexical disambiguation of the genus terms is one that has proven to be very difficult. In COLING 90 we described our initial work on the automatic creation of a taxonomy of noun senses from Longmans Dictionary of Contemporary English (LDOCE). The algorithm for lexical disambiguation of the genus term was accurate about 80% of the time and made use of the semantic categories, the subject area markings and the frequency of use information in LDOCE. In this paper we report a series of experiments which weight the three factors in various ways, and describe our improvements to the algorithm (to about 90% accuracy).

Selecting Decomposable Models for Word-Sense Disambiguation: TheGrling-Sdm System

This paper describes the grling-sdm system, which is asupervised probabilistic classifier that participated in the 1998SENSEVAL competition for word-sense disambiguation. This systemuses model search to select decomposable probability models describingthe dependencies among the feature variables.These types of models have been found to be advantageous in terms ofefficiency and representational power. Performance on the SENSEVALevaluation data is discussed.

A new approach to word sense disambiguation

This paper presents and evaluates models created according to a schema that provides a description of the joint distribution of the values of sense tags and contextual features that is potentially applicable to a wide range of content words. The models are evaluated through a series of experiments, the results of which suggest that the schema is particularly well suited to nouns but that it is also applicable to words in other syntactic categories.

Make space for the Pi

The Raspberry Pi is an inexpensive computing system that can play an essential part of any computing curriculum. Since its release in 2012, the Raspberry Pi has been infiltrating K-12 education; it has the potential to make coding in K-12 schools as commonplace as textbooks. It has also changed the playing field for hobbyists by offering a low-priced general-purpose computing system that challenges the Arduino in terms of open source support. In this paper, we advocate using the Raspberry Pi (RPi) throughout the University computing curricula as well. Low-priced and portable, the RPi is an exposed hardware platform students can tinker with without fear of breaking. Properly used, it affords students the opportunity to experimentally discover many aspects of computing. In this paper, we discuss the aspects of the RPi that make it appropriate for a University computing curriculum. We describe our classroom experiences and laboratory best practices as well as survey the work of others involved in integrated the RPi into University curriculums.